




'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import LevitImageProcessor

class 	_snake_case(			unittest.TestCase      ):






 def __init__(self       :  Union[str, Any]    ,		a       :  Dict    ,		a       :  Any=7    ,		a       :  Tuple=3    ,		a       :  Tuple=18    ,		a       :  Tuple=30    ,		a       :  Optional[int]=4_00    ,		a       :  str=True    ,		a       :  List[Any]=None    ,		a       :  Tuple=True    ,		a       :  Optional[int]=None    ,		a       :  Tuple=True    ,		a       :  int=[0.5, 0.5, 0.5]    ,		a       :  List[Any]=[0.5, 0.5, 0.5]    ,		)       ->   Union[str, Any]:






    """simple docstring"""


    A__       =     size if size is not None else {'shortest_edge': 18}
    A__       =     crop_size if crop_size is not None else {'height': 18, 'width': 18}
    A__       =     parent
    A__       =     batch_size
    A__       =     num_channels
    A__       =     image_size
    A__       =     min_resolution
    A__       =     max_resolution
    A__       =     do_resize
    A__       =     size
    A__       =     do_center_crop
    A__       =     crop_size
    A__       =     do_normalize
    A__       =     image_mean
    A__       =     image_std






 def 						_UpperCamelCase (self       :  Dict    )       ->   str:






    """simple docstring"""


    return {
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_normalize": self.do_normalize,
        "do_resize": self.do_resize,
        "do_center_crop": self.do_center_crop,
        "size": self.size,
        "crop_size": self.crop_size,
    }




@require_torch
@require_vision
class 	_snake_case(			UpperCAmelCase       , unittest.TestCase      ):
 __snake_case:							str						=					LevitImageProcessor if is_vision_available() else None






 def 						_UpperCamelCase (self       :  List[str]    )       ->   List[str]:






    """simple docstring"""


    A__       =     LevitImageProcessingTester(self    )






 @property
 def 						_UpperCamelCase (self       :  List[Any]    )       ->   Optional[int]:






    """simple docstring"""


    return self.image_processor_tester.prepare_image_processor_dict()






 def 						_UpperCamelCase (self       :  Tuple    )       ->   Any:






    """simple docstring"""


    A__       =     self.image_processing_class(**self.image_processor_dict    )
    self.assertTrue(hasattr(a    ,		'image_mean'    )    )
    self.assertTrue(hasattr(a    ,		'image_std'    )    )
    self.assertTrue(hasattr(a    ,		'do_normalize'    )    )
    self.assertTrue(hasattr(a    ,		'do_resize'    )    )
    self.assertTrue(hasattr(a    ,		'do_center_crop'    )    )
    self.assertTrue(hasattr(a    ,		'size'    )    )






 def 						_UpperCamelCase (self       :  int    )       ->   Tuple:






    """simple docstring"""


    A__       =     self.image_processing_class.from_dict(self.image_processor_dict    )
    self.assertEqual(image_processor.size    ,		{'shortest_edge': 18}    )
    self.assertEqual(image_processor.crop_size    ,		{'height': 18, 'width': 18}    )

    A__       =     self.image_processing_class.from_dict(self.image_processor_dict    ,		size=42    ,		crop_size=84    )
    self.assertEqual(image_processor.size    ,		{'shortest_edge': 42}    )
    self.assertEqual(image_processor.crop_size    ,		{'height': 84, 'width': 84}    )






 def 						_UpperCamelCase (self       :  List[Any]    )       ->   int:






    """simple docstring"""


    pass






 def 						_UpperCamelCase (self       :  Any    )       ->   Optional[int]:






    """simple docstring"""


    A__       =     self.image_processing_class(**self.image_processor_dict    )
    # create random PIL images
    A__       =     prepare_image_inputs(self.image_processor_tester    ,		equal_resolution=a    )
    for image in image_inputs:
       self.assertIsInstance(a    ,		Image.Image    )

    # Test not batched input
    A__       =     image_processing(image_inputs[0]    ,		return_tensors='pt'    ).pixel_values
    self.assertEqual(
        encoded_images.shape    ,		(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )    ,		)

    # Test batched
    A__       =     image_processing(a    ,		return_tensors='pt'    ).pixel_values
    self.assertEqual(
        encoded_images.shape    ,		(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )    ,		)






 def 						_UpperCamelCase (self       :  Optional[Any]    )       ->   Tuple:






    """simple docstring"""


    A__       =     self.image_processing_class(**self.image_processor_dict    )
    # create random numpy tensors
    A__       =     prepare_image_inputs(self.image_processor_tester    ,		equal_resolution=a    ,		numpify=a    )
    for image in image_inputs:
       self.assertIsInstance(a    ,		np.ndarray    )

    # Test not batched input
    A__       =     image_processing(image_inputs[0]    ,		return_tensors='pt'    ).pixel_values
    self.assertEqual(
        encoded_images.shape    ,		(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )    ,		)

    # Test batched
    A__       =     image_processing(a    ,		return_tensors='pt'    ).pixel_values
    self.assertEqual(
        encoded_images.shape    ,		(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )    ,		)






 def 						_UpperCamelCase (self       :  Tuple    )       ->   str:






    """simple docstring"""


    A__       =     self.image_processing_class(**self.image_processor_dict    )
    # create random PyTorch tensors
    A__       =     prepare_image_inputs(self.image_processor_tester    ,		equal_resolution=a    ,		torchify=a    )
    for image in image_inputs:
       self.assertIsInstance(a    ,		torch.Tensor    )

    # Test not batched input
    A__       =     image_processing(image_inputs[0]    ,		return_tensors='pt'    ).pixel_values
    self.assertEqual(
        encoded_images.shape    ,		(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )    ,		)

    # Test batched
    A__       =     image_processing(a    ,		return_tensors='pt'    ).pixel_values
    self.assertEqual(
        encoded_images.shape    ,		(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )    ,		)








'''simple docstring'''







import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_     						=				logging.get_logger(__name__)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase=False						):






   '''simple docstring'''







   A__       =     []
   for i in range(config.num_hidden_layers						):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""deit.encoder.layer.{i}.layernorm_before.weight""")						)
      rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""deit.encoder.layer.{i}.layernorm_before.bias""")						)
      rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""deit.encoder.layer.{i}.attention.output.dense.weight""")						)
      rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""deit.encoder.layer.{i}.attention.output.dense.bias""")						)
      rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""deit.encoder.layer.{i}.layernorm_after.weight""")						)
      rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""deit.encoder.layer.{i}.layernorm_after.bias""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""deit.encoder.layer.{i}.intermediate.dense.weight""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""deit.encoder.layer.{i}.intermediate.dense.bias""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""deit.encoder.layer.{i}.output.dense.weight""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""deit.encoder.layer.{i}.output.dense.bias""")						)

   # projection layer + position embeddings
   rename_keys.extend(
       [
           ('cls_token', 'deit.embeddings.cls_token'),
           ('dist_token', 'deit.embeddings.distillation_token'),
           ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
           ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
           ('pos_embed', 'deit.embeddings.position_embeddings'),
       ]						)

   if base_model:
      # layernorm + pooler
      rename_keys.extend(
          [
              ('norm.weight', 'layernorm.weight'),
              ('norm.bias', 'layernorm.bias'),
              ('pre_logits.fc.weight', 'pooler.dense.weight'),
              ('pre_logits.fc.bias', 'pooler.dense.bias'),
          ]						)

      # if just the base model, we should remove "deit" from all keys that start with "deit"
      A__       =     [(pair[0], pair[1][4:]) if pair[1].startswith('deit'						) else pair for pair in rename_keys]
   else:
      # layernorm + classification heads
      rename_keys.extend(
          [
              ('norm.weight', 'deit.layernorm.weight'),
              ('norm.bias', 'deit.layernorm.bias'),
              ('head.weight', 'cls_classifier.weight'),
              ('head.bias', 'cls_classifier.bias'),
              ('head_dist.weight', 'distillation_classifier.weight'),
              ('head_dist.bias', 'distillation_classifier.bias'),
          ]						)

   return rename_keys







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=False						):






   '''simple docstring'''







   for i in range(config.num_hidden_layers						):
      if base_model:
         A__       =     ''
      else:
         A__       =     'deit.'
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      A__       =     state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""						)
      A__       =     state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""						)
      # next, add query, keys and values (in that order) to the state dict
      A__       =     in_proj_weight[
          : config.hidden_size, :
      ]
      A__       =     in_proj_bias[: config.hidden_size]
      A__       =     in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      A__       =     in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      A__       =     in_proj_weight[
          -config.hidden_size :, :
      ]
      A__       =     in_proj_bias[-config.hidden_size :]







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     dct.pop(UpperCAmelCase						)
   A__       =     val







def 						_A      ( ):






   '''simple docstring'''







   A__       =     'http://images.cocodataset.org/val2017/000000039769.jpg'
   A__       =     Image.open(requests.get(UpperCAmelCase      ,stream=UpperCAmelCase						).raw						)
   return im







@torch.no_grad()
def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     DeiTConfig()
   # all deit models have fine-tuned heads
   A__       =     False
   # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
   A__       =     1000
   A__       =     'huggingface/label-files'
   A__       =     'imagenet-1k-id2label.json'
   A__       =     json.load(open(hf_hub_download(UpperCAmelCase      ,UpperCAmelCase      ,repo_type='dataset'						)      ,'r'						)						)
   A__       =     {int(UpperCAmelCase						): v for k, v in idalabel.items()}
   A__       =     idalabel
   A__       =     {v: k for k, v in idalabel.items()}
   A__       =     int(deit_name[-6:-4]						)
   A__       =     int(deit_name[-3:]						)
   # size of the architecture
   if deit_name[9:].startswith('tiny'						):
      A__       =     192
      A__       =     768
      A__       =     12
      A__       =     3
   elif deit_name[9:].startswith('small'						):
      A__       =     384
      A__       =     1536
      A__       =     12
      A__       =     6
   if deit_name[9:].startswith('base'						):
      pass
   elif deit_name[4:].startswith('large'						):
      A__       =     1024
      A__       =     4096
      A__       =     24
      A__       =     16

   # load original model from timm
   A__       =     timm.create_model(UpperCAmelCase      ,pretrained=UpperCAmelCase						)
   timm_model.eval()

   # load state_dict of original model, remove and rename some keys
   A__       =     timm_model.state_dict()
   A__       =     create_rename_keys(UpperCAmelCase      ,UpperCAmelCase						)
   for src, dest in rename_keys:
      rename_key(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)
   read_in_q_k_v(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)

   # load HuggingFace model
   A__       =     DeiTForImageClassificationWithTeacher(UpperCAmelCase						).eval()
   model.load_state_dict(UpperCAmelCase						)

   # Check outputs on an image, prepared by DeiTImageProcessor
   A__       =     int(
       (256 / 224) * config.image_size						)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
   A__       =     DeiTImageProcessor(size=UpperCAmelCase      ,crop_size=config.image_size						)
   A__       =     image_processor(images=prepare_img()      ,return_tensors='pt'						)
   A__       =     encoding['pixel_values']
   A__       =     model(UpperCAmelCase						)

   A__       =     timm_model(UpperCAmelCase						)
   assert timm_logits.shape == outputs.logits.shape
   assert torch.allclose(UpperCAmelCase      ,outputs.logits      ,atol=1e-3						)

   Path(UpperCAmelCase						).mkdir(exist_ok=UpperCAmelCase						)
   print(F"""Saving model {deit_name} to {pytorch_dump_folder_path}"""						)
   model.save_pretrained(UpperCAmelCase						)
   print(F"""Saving image processor to {pytorch_dump_folder_path}"""						)
   image_processor.save_pretrained(UpperCAmelCase						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--deit_name''',
        default='''vit_deit_base_distilled_patch16_224''',
        type=str,
        help='''Name of the DeiT timm model you\'d like to convert.''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
    )

    lowerCAmelCase_     						=				parser.parse_args()
    convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)


